| Budget Amount *help |
¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2011: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Research Abstract |
In this study, we proposed new search algorithms to efficiently find good solutions in globally multimodal space. It is required that, in the globally multimodal space, search algorithms efficiently find promising big valleys and efficiently search the best solution in each big valley. In order to achieve this requirement, we proposed a framework for finding big valleys and several real-coded evolutionary algorithms for searching in a big valley. The proposed framework iteratively executes real-coded evolutionary algorithms and efficiently find new big valleys by using a history of search regions. We confirmed that the proposed framework with a real-coded genetic algorithm called AREX/JGG succeeded in finding the optima that the conventional methods failed to find on globally multimodal benchmark functions. We also confirmed that the proposed real-coded evolutionary algorithms for searching in a big valley outperformed state-of-the-art algorithms on well-known benchmark functions.
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